leFred and Scott sit down with Gaurav Chadha to explore MySQL AI, a new solution that brings advanced AI features available in HeatWave to organizations running MySQL Enterprise Edition on-premises. Discover how MySQL AI bridges the gap between cloud innovation and on-premise infrastructure, making transformative AI capabilities more accessible, secure, and efficient for teams that rely on MySQL Enterprise Edition wherever their databases reside.

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Episode Transcript:

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Welcome to Inside MySQL: Sakila Speaks, a podcast dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL product updates and insightful interviews with members of the MySQL community.

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Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started.

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Welcome back to another episode of Inside MySQL: Sakila Speaks. Hi, I'm LeFred.

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And I'm Scott Stroz.

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Today, we are thrilled to have Guarav Chadha joining us.

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Guarav is a Senior Development Manager leading development of MySQL HeatWave Lakehouse with a keen interest in systems, machine learning and computer architecture.

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Guarav brings a multifaceted expertise to database technology. Following the completion of his PhD from the University of Michigan, Ann Arbor, Guarav started at Oracle Labs in 2016, working on a research project which eventually graduated into MySQL HeatWave.

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But today we will talk with him about MySQL and AI on premise. Welcome Guarav.

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Thanks, Fred. Hi, Scott.

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Hi, Guarav. How are you?

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Doing good.

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So we're going to dive right in. And AI, we see AI is taking over the world. It's being touted for the solution to everything.

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How do you see AI transforming traditional on-premise database environments, especially in enterprise setups?

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Yes, Scott. So, I completely agree. AI is a transformational technology, and it has the potential to improve everything that we see around us.

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So, with regards to traditional on-premise database environments, especially in enterprise setups, I see multiple categories here. So, AI is a technology and a toolset.

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And like many other operators in databases, it can help with more and different data analysis. So, think of AI as a new set of SQL operators, which can tease out or analyze data and derive insights that are hard to do it with other operators, with other analysis tools.

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And hard for folks to call up. And hard for folks to code up. And that's where I think AI enhances it very easily enters into the database environments.

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What I mean by that is examples are recommendation systems, anomaly detection, so on and so forth.

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The other category is what I would say user assistance.

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So, not everyone is a SQL expert. And we want database technology and databases to be accessible to more people who may or may not come from a traditional database background.

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And SQL is a very powerful language and where it can be daunting to start with.

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So, again, this is a general category where maybe folks who are not very familiar with a specific programming language like SQL could write things out in just plain natural text.

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And AI tools could translate this into a programmatic interface or programmatic language or SQL directly.

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And that's another facet where I think AI can make database systems more approachable to a larger category of folks.

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It can also give you more user friendly responses, like instead of saying, oh, here's the error code, something went wrong.

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It can give you more information, more user friendly responses.

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So those are some examples of where I would say the second category, user assistance.

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The third category of where AI could help is database management.

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So databases are systems of record, the sources of truth and have a very high bar of staying up and being available.

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AI can help schedule maintenance at the right time where maybe the workload is low.

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They can predict things that might get slow.

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We have a whole area called predictive maintenance and make databases more highly available, more easily approachable.

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Thank you.

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This sounds very interesting.

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And because we are talking about MySQL on-prem, right?

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So from these categories, what features could we expect then one day to see in MySQL enterprise with AI or for AI?

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So what can you tell us about that?

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So for MySQL AI, we are bringing a whole host of AI features to on-premise MySQL deployments.

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And we will lean heavily with this first version on the first category, which is data analysis.

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How can AI help with data analysis?

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And within this, I would focus on, I would say, a few subcategories.

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The first is, with AI and generative AI specifically, it has brought the industry a new tool set to search through and understand documents.

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And not just structured data or relational data, just plain documents, which is true for a lot of enterprise companies.

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Companies have years and years worth of information stored in documents, in PDF documents, in HTML documents, and not really put into a database necessarily.

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And this has always been hard to search.

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It has been very manual, it has been very hard to bring to a database and perform a very fast and meaningful search.

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With generative AI and what we call vector store and vector search, you can search through unstructured data like documents, semantically, instead of just through keywords.

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You can search them by meaning.

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That's a very powerful technology that we are bringing to MySQL AI.

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So if users have documents in their file systems, they can ingest them into the database, and we will automatically create what we call a vector store out of it, which prepares the data in these documents to be searched semantically.

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Obviously, in order to this, we are adding a new operator, which does this semantic search, we call this vector distance.

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Additionally, I spoke about data analysis tools like recommendation systems, like anomaly detection, and these operators also being brought to MySQL AI, where you can plug them into your logs, or you can plug them into other metrics.

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And figure out when things can go wrong, or any other domain that is useful.

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An example of a domain for anomaly detection would be financial fraud, credit card fraud.

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So it's very useful in those scenarios.

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And the last category I would say among data analysis is generative AI.

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We're bringing LLMs to MySQL AI, and the power of LLMs really is they can generate new data and new user-friendly text from just bullet points, for instance.

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So not just analyzing data, but generating new data is possible through LLMs.

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So that I would say covers the first category.

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This is all data analysis.

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Among the second category, which is user assistance.

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User assistance is by bringing LLMs to on-premise MySQL AI deployments.

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It gives the user freedom to build more user-friendly applications or make the existing applications more user-friendly.

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And this is what we will start with, with version one of MySQL AI.

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So are there any specific features in MySQL Enterprise, like Firewall or Enterprise Audit, that support AI-enabled applications?

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So as we discussed, AI is an incredibly powerful set of tools and technologies.

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And this is our first salvo in enabling our customers to build and augment applications using AI.

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So we're bringing a whole tool set, we're bringing faster data analysis, more meaningful and different kinds of data analysis to help users build and augment existing applications